[Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.

### Installation

Elasticsearch support requires additional dependencies. Install them with:

```bash
pip install elasticsearch>=8.0.0
```

### Usage

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "elasticsearch",
        "config": {
            "collection_name": "mem0",
            "host": "localhost",
            "port": 9200,
            "embedding_model_dims": 1536
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

### Config

Let's see the available parameters for the `elasticsearch` config:

| Parameter              | Description                                        | Default Value |
| ---------------------- | -------------------------------------------------- | ------------- |
| `collection_name`      | The name of the index to store the vectors         | `mem0`        |
| `embedding_model_dims` | Dimensions of the embedding model                  | `1536`        |
| `host`                 | The host where the Elasticsearch server is running | `localhost`   |
| `port`                 | The port where the Elasticsearch server is running | `9200`        |
| `cloud_id`             | Cloud ID for Elastic Cloud deployment              | `None`        |
| `api_key`              | API key for authentication                         | `None`        |
| `user`                 | Username for basic authentication                  | `None`        |
| `password`             | Password for basic authentication                  | `None`        |
| `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
| `auto_create_index`    | Whether to automatically create the index          | `True`        |

### Features

- Efficient vector search using Elasticsearch's native k-NN search
- Support for both local and cloud deployments (Elastic Cloud)
- Multiple authentication methods (Basic Auth, API Key)
- Automatic index creation with optimized mappings for vector search
- Memory isolation through payload filtering
